There are three properties being used here:
- the determinant of a product of square matrices of equal size is equal to the product of the determinants of its square, equally sized factors
- the logarithm of a product is equal to the sum of the logarithms of its factors
- the determinant of a matrix is equal to the product of its eigenvalues.
Let's start from
\begin{align}
g(t) = \log \det (Z^{1/2}(I + tZ^{-1/2}VZ^{-1/2})Z^{1/2}).
\end{align}
The determinant product property yields
\begin{align}
g(t) = \log\Big(\det(Z^{1/2})\det(I + tZ^{-1/2}VZ^{-1/2})\det(Z^{1/2})\Big),
\end{align}
and by commutativity of multiplication, we can rearrange it so that
\begin{align}
g(t) &= \log\Big(\det(Z^{1/2})\det(Z^{1/2})\det(I + tZ^{-1/2}VZ^{-1/2})\Big), \\
&= \log\Big(\det(I + tZ^{-1/2}VZ^{-1/2})\det(Z)\Big).
\end{align}
Using the product-sum property of logarithms, this expression becomes
\begin{align}
g(t) &= \log\det(I + tZ^{-1/2}VZ^{-1/2}) + \log\det(Z),
\end{align}
and we're most of the way there with the $\det(Z)$ term already separated out.
If the eigenvalues of $Z^{-1/2}VZ^{-1/2}$ are $\lambda_{i}$, then:
- the eigenvalues of $tZ^{-1/2}VZ^{-1/2}$ are $t\lambda_{i}$, because the eigenvectors of each matrix are the same
- the eigenvalues of $I + tZ^{-1/2}VZ^{-1/2}$ are $(1 + t\lambda_{i})$ because the eigenvectors of $Z^{-1/2}VZ^{-1/2}$ will also be eigenvectors for $I$.
This aside about eigenvalues is important, because to complete the derivation, we need to use the property that the determinant of a matrix is the product of its eigenvalues:
\begin{align}
g(t) &= \log\left(\prod_{i=1}^{n}(1+t\lambda_{i})\right) + \log\det(Z).
\end{align}
To complete the derivation, we use the product-sum property of logarithms (again), and get
\begin{align}
g(t) = \sum_{i=1}^{n} \log(1 + t\lambda_{i}) + \log\det(Z).
\end{align}